{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Guided Project: Storytelling Data Visualization on Exchange Rates\n",
    "## Introducing the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Period\\Unit:</th>\n",
       "      <th>[Australian dollar ]</th>\n",
       "      <th>[Bulgarian lev ]</th>\n",
       "      <th>[Brazilian real ]</th>\n",
       "      <th>[Canadian dollar ]</th>\n",
       "      <th>[Swiss franc ]</th>\n",
       "      <th>[Chinese yuan renminbi ]</th>\n",
       "      <th>[Cypriot pound ]</th>\n",
       "      <th>[Czech koruna ]</th>\n",
       "      <th>[Danish krone ]</th>\n",
       "      <th>...</th>\n",
       "      <th>[Romanian leu ]</th>\n",
       "      <th>[Russian rouble ]</th>\n",
       "      <th>[Swedish krona ]</th>\n",
       "      <th>[Singapore dollar ]</th>\n",
       "      <th>[Slovenian tolar ]</th>\n",
       "      <th>[Slovak koruna ]</th>\n",
       "      <th>[Thai baht ]</th>\n",
       "      <th>[Turkish lira ]</th>\n",
       "      <th>[US dollar ]</th>\n",
       "      <th>[South African rand ]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-08</td>\n",
       "      <td>1.5758</td>\n",
       "      <td>1.9558</td>\n",
       "      <td>6.5748</td>\n",
       "      <td>1.5543</td>\n",
       "      <td>1.0827</td>\n",
       "      <td>7.9184</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.163</td>\n",
       "      <td>7.4369</td>\n",
       "      <td>...</td>\n",
       "      <td>4.8708</td>\n",
       "      <td>90.8000</td>\n",
       "      <td>10.0510</td>\n",
       "      <td>1.6228</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.8480</td>\n",
       "      <td>9.0146</td>\n",
       "      <td>1.2250</td>\n",
       "      <td>18.7212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>1.5836</td>\n",
       "      <td>1.9558</td>\n",
       "      <td>6.5172</td>\n",
       "      <td>1.5601</td>\n",
       "      <td>1.0833</td>\n",
       "      <td>7.9392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.147</td>\n",
       "      <td>7.4392</td>\n",
       "      <td>...</td>\n",
       "      <td>4.8712</td>\n",
       "      <td>91.2000</td>\n",
       "      <td>10.0575</td>\n",
       "      <td>1.6253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.8590</td>\n",
       "      <td>8.9987</td>\n",
       "      <td>1.2276</td>\n",
       "      <td>18.7919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-06</td>\n",
       "      <td>1.5824</td>\n",
       "      <td>1.9558</td>\n",
       "      <td>6.5119</td>\n",
       "      <td>1.5640</td>\n",
       "      <td>1.0821</td>\n",
       "      <td>7.9653</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.145</td>\n",
       "      <td>7.4393</td>\n",
       "      <td>...</td>\n",
       "      <td>4.8720</td>\n",
       "      <td>90.8175</td>\n",
       "      <td>10.0653</td>\n",
       "      <td>1.6246</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.9210</td>\n",
       "      <td>9.0554</td>\n",
       "      <td>1.2338</td>\n",
       "      <td>18.5123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>1.5927</td>\n",
       "      <td>1.9558</td>\n",
       "      <td>6.5517</td>\n",
       "      <td>1.5651</td>\n",
       "      <td>1.0803</td>\n",
       "      <td>7.9315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.227</td>\n",
       "      <td>7.4387</td>\n",
       "      <td>...</td>\n",
       "      <td>4.8721</td>\n",
       "      <td>91.6715</td>\n",
       "      <td>10.0570</td>\n",
       "      <td>1.6180</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.7760</td>\n",
       "      <td>9.0694</td>\n",
       "      <td>1.2271</td>\n",
       "      <td>18.4194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>1.5928</td>\n",
       "      <td>1.9558</td>\n",
       "      <td>6.3241</td>\n",
       "      <td>1.5621</td>\n",
       "      <td>1.0811</td>\n",
       "      <td>7.9484</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.141</td>\n",
       "      <td>7.4379</td>\n",
       "      <td>...</td>\n",
       "      <td>4.8713</td>\n",
       "      <td>90.3420</td>\n",
       "      <td>10.0895</td>\n",
       "      <td>1.6198</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.7280</td>\n",
       "      <td>9.0579</td>\n",
       "      <td>1.2296</td>\n",
       "      <td>17.9214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
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       "  Period\\Unit: [Australian dollar ] [Bulgarian lev ] [Brazilian real ]  \\\n",
       "0   2021-01-08               1.5758           1.9558            6.5748   \n",
       "1   2021-01-07               1.5836           1.9558            6.5172   \n",
       "2   2021-01-06               1.5824           1.9558            6.5119   \n",
       "3   2021-01-05               1.5927           1.9558            6.5517   \n",
       "4   2021-01-04               1.5928           1.9558            6.3241   \n",
       "\n",
       "  [Canadian dollar ] [Swiss franc ] [Chinese yuan renminbi ] [Cypriot pound ]  \\\n",
       "0             1.5543         1.0827                   7.9184              NaN   \n",
       "1             1.5601         1.0833                   7.9392              NaN   \n",
       "2             1.5640         1.0821                   7.9653              NaN   \n",
       "3             1.5651         1.0803                   7.9315              NaN   \n",
       "4             1.5621         1.0811                   7.9484              NaN   \n",
       "\n",
       "  [Czech koruna ] [Danish krone ]  ... [Romanian leu ] [Russian rouble ]  \\\n",
       "0          26.163          7.4369  ...          4.8708           90.8000   \n",
       "1          26.147          7.4392  ...          4.8712           91.2000   \n",
       "2          26.145          7.4393  ...          4.8720           90.8175   \n",
       "3          26.227          7.4387  ...          4.8721           91.6715   \n",
       "4          26.141          7.4379  ...          4.8713           90.3420   \n",
       "\n",
       "  [Swedish krona ] [Singapore dollar ] [Slovenian tolar ] [Slovak koruna ]  \\\n",
       "0          10.0510              1.6228                NaN              NaN   \n",
       "1          10.0575              1.6253                NaN              NaN   \n",
       "2          10.0653              1.6246                NaN              NaN   \n",
       "3          10.0570              1.6180                NaN              NaN   \n",
       "4          10.0895              1.6198                NaN              NaN   \n",
       "\n",
       "  [Thai baht ] [Turkish lira ] [US dollar ]  [South African rand ]  \n",
       "0      36.8480          9.0146       1.2250                18.7212  \n",
       "1      36.8590          8.9987       1.2276                18.7919  \n",
       "2      36.9210          9.0554       1.2338                18.5123  \n",
       "3      36.7760          9.0694       1.2271                18.4194  \n",
       "4      36.7280          9.0579       1.2296                17.9214  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "exchange_rates = pd.read_csv('euro-daily-hist_1999_2020.csv')\n",
    "exchange_rates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Period\\Unit:</th>\n",
       "      <th>[Australian dollar ]</th>\n",
       "      <th>[Bulgarian lev ]</th>\n",
       "      <th>[Brazilian real ]</th>\n",
       "      <th>[Canadian dollar ]</th>\n",
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       "      <th>[Russian rouble ]</th>\n",
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       "      <th>[Singapore dollar ]</th>\n",
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       "      <th>5694</th>\n",
       "      <td>1999-01-08</td>\n",
       "      <td>1.8406</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1.7643</td>\n",
       "      <td>1.6138</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>34.938</td>\n",
       "      <td>7.4433</td>\n",
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       "      <td>1.3143</td>\n",
       "      <td>27.2075</td>\n",
       "      <td>9.1650</td>\n",
       "      <td>1.9537</td>\n",
       "      <td>188.8400</td>\n",
       "      <td>42.560</td>\n",
       "      <td>42.5590</td>\n",
       "      <td>0.3718</td>\n",
       "      <td>1.1659</td>\n",
       "      <td>6.7855</td>\n",
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       "    <tr>\n",
       "      <th>5695</th>\n",
       "      <td>1999-01-07</td>\n",
       "      <td>1.8474</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7602</td>\n",
       "      <td>1.6165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.58187</td>\n",
       "      <td>34.886</td>\n",
       "      <td>7.4431</td>\n",
       "      <td>...</td>\n",
       "      <td>1.3092</td>\n",
       "      <td>26.9876</td>\n",
       "      <td>9.1800</td>\n",
       "      <td>1.9436</td>\n",
       "      <td>188.8000</td>\n",
       "      <td>42.765</td>\n",
       "      <td>42.1678</td>\n",
       "      <td>0.3701</td>\n",
       "      <td>1.1632</td>\n",
       "      <td>6.8283</td>\n",
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       "    <tr>\n",
       "      <th>5696</th>\n",
       "      <td>1999-01-06</td>\n",
       "      <td>1.8820</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7711</td>\n",
       "      <td>1.6116</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.58200</td>\n",
       "      <td>34.850</td>\n",
       "      <td>7.4452</td>\n",
       "      <td>...</td>\n",
       "      <td>1.3168</td>\n",
       "      <td>27.4315</td>\n",
       "      <td>9.3050</td>\n",
       "      <td>1.9699</td>\n",
       "      <td>188.7000</td>\n",
       "      <td>42.778</td>\n",
       "      <td>42.6949</td>\n",
       "      <td>0.3722</td>\n",
       "      <td>1.1743</td>\n",
       "      <td>6.7307</td>\n",
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       "    <tr>\n",
       "      <th>5697</th>\n",
       "      <td>1999-01-05</td>\n",
       "      <td>1.8944</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7965</td>\n",
       "      <td>1.6123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.58230</td>\n",
       "      <td>34.917</td>\n",
       "      <td>7.4495</td>\n",
       "      <td>...</td>\n",
       "      <td>1.3168</td>\n",
       "      <td>26.5876</td>\n",
       "      <td>9.4025</td>\n",
       "      <td>1.9655</td>\n",
       "      <td>188.7750</td>\n",
       "      <td>42.848</td>\n",
       "      <td>42.5048</td>\n",
       "      <td>0.3728</td>\n",
       "      <td>1.1790</td>\n",
       "      <td>6.7975</td>\n",
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       "    <tr>\n",
       "      <th>5698</th>\n",
       "      <td>1999-01-04</td>\n",
       "      <td>1.9100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.8004</td>\n",
       "      <td>1.6168</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.58231</td>\n",
       "      <td>35.107</td>\n",
       "      <td>7.4501</td>\n",
       "      <td>...</td>\n",
       "      <td>1.3111</td>\n",
       "      <td>25.2875</td>\n",
       "      <td>9.4696</td>\n",
       "      <td>1.9554</td>\n",
       "      <td>189.0450</td>\n",
       "      <td>42.991</td>\n",
       "      <td>42.6799</td>\n",
       "      <td>0.3723</td>\n",
       "      <td>1.1789</td>\n",
       "      <td>6.9358</td>\n",
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       "     Period\\Unit: [Australian dollar ] [Bulgarian lev ] [Brazilian real ]  \\\n",
       "5694   1999-01-08               1.8406              NaN               NaN   \n",
       "5695   1999-01-07               1.8474              NaN               NaN   \n",
       "5696   1999-01-06               1.8820              NaN               NaN   \n",
       "5697   1999-01-05               1.8944              NaN               NaN   \n",
       "5698   1999-01-04               1.9100              NaN               NaN   \n",
       "\n",
       "     [Canadian dollar ] [Swiss franc ] [Chinese yuan renminbi ]  \\\n",
       "5694             1.7643         1.6138                      NaN   \n",
       "5695             1.7602         1.6165                      NaN   \n",
       "5696             1.7711         1.6116                      NaN   \n",
       "5697             1.7965         1.6123                      NaN   \n",
       "5698             1.8004         1.6168                      NaN   \n",
       "\n",
       "     [Cypriot pound ] [Czech koruna ] [Danish krone ]  ... [Romanian leu ]  \\\n",
       "5694          0.58187          34.938          7.4433  ...          1.3143   \n",
       "5695          0.58187          34.886          7.4431  ...          1.3092   \n",
       "5696          0.58200          34.850          7.4452  ...          1.3168   \n",
       "5697          0.58230          34.917          7.4495  ...          1.3168   \n",
       "5698          0.58231          35.107          7.4501  ...          1.3111   \n",
       "\n",
       "     [Russian rouble ] [Swedish krona ] [Singapore dollar ]  \\\n",
       "5694           27.2075           9.1650              1.9537   \n",
       "5695           26.9876           9.1800              1.9436   \n",
       "5696           27.4315           9.3050              1.9699   \n",
       "5697           26.5876           9.4025              1.9655   \n",
       "5698           25.2875           9.4696              1.9554   \n",
       "\n",
       "     [Slovenian tolar ] [Slovak koruna ] [Thai baht ] [Turkish lira ]  \\\n",
       "5694           188.8400           42.560      42.5590          0.3718   \n",
       "5695           188.8000           42.765      42.1678          0.3701   \n",
       "5696           188.7000           42.778      42.6949          0.3722   \n",
       "5697           188.7750           42.848      42.5048          0.3728   \n",
       "5698           189.0450           42.991      42.6799          0.3723   \n",
       "\n",
       "     [US dollar ]  [South African rand ]  \n",
       "5694       1.1659                 6.7855  \n",
       "5695       1.1632                 6.8283  \n",
       "5696       1.1743                 6.7307  \n",
       "5697       1.1790                 6.7975  \n",
       "5698       1.1789                 6.9358  \n",
       "\n",
       "[5 rows x 41 columns]"
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     },
     "execution_count": 2,
     "metadata": {},
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   ],
   "source": [
    "exchange_rates.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5699 entries, 0 to 5698\n",
      "Data columns (total 41 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Period\\Unit:              5699 non-null   object \n",
      " 1   [Australian dollar ]      5699 non-null   object \n",
      " 2   [Bulgarian lev ]          5297 non-null   object \n",
      " 3   [Brazilian real ]         5431 non-null   object \n",
      " 4   [Canadian dollar ]        5699 non-null   object \n",
      " 5   [Swiss franc ]            5699 non-null   object \n",
      " 6   [Chinese yuan renminbi ]  5431 non-null   object \n",
      " 7   [Cypriot pound ]          2346 non-null   object \n",
      " 8   [Czech koruna ]           5699 non-null   object \n",
      " 9   [Danish krone ]           5699 non-null   object \n",
      " 10  [Estonian kroon ]         3130 non-null   object \n",
      " 11  [UK pound sterling ]      5699 non-null   object \n",
      " 12  [Greek drachma ]          520 non-null    object \n",
      " 13  [Hong Kong dollar ]       5699 non-null   object \n",
      " 14  [Croatian kuna ]          5431 non-null   object \n",
      " 15  [Hungarian forint ]       5699 non-null   object \n",
      " 16  [Indonesian rupiah ]      5699 non-null   object \n",
      " 17  [Israeli shekel ]         5431 non-null   object \n",
      " 18  [Indian rupee ]           5431 non-null   object \n",
      " 19  [Iceland krona ]          3292 non-null   float64\n",
      " 20  [Japanese yen ]           5699 non-null   object \n",
      " 21  [Korean won ]             5699 non-null   object \n",
      " 22  [Lithuanian litas ]       4159 non-null   object \n",
      " 23  [Latvian lats ]           3904 non-null   object \n",
      " 24  [Maltese lira ]           2346 non-null   object \n",
      " 25  [Mexican peso ]           5699 non-null   object \n",
      " 26  [Malaysian ringgit ]      5699 non-null   object \n",
      " 27  [Norwegian krone ]        5699 non-null   object \n",
      " 28  [New Zealand dollar ]     5699 non-null   object \n",
      " 29  [Philippine peso ]        5699 non-null   object \n",
      " 30  [Polish zloty ]           5699 non-null   object \n",
      " 31  [Romanian leu ]           5637 non-null   float64\n",
      " 32  [Russian rouble ]         5699 non-null   object \n",
      " 33  [Swedish krona ]          5699 non-null   object \n",
      " 34  [Singapore dollar ]       5699 non-null   object \n",
      " 35  [Slovenian tolar ]        2085 non-null   object \n",
      " 36  [Slovak koruna ]          2608 non-null   object \n",
      " 37  [Thai baht ]              5699 non-null   object \n",
      " 38  [Turkish lira ]           5637 non-null   float64\n",
      " 39  [US dollar ]              5699 non-null   object \n",
      " 40  [South African rand ]     5699 non-null   object \n",
      "dtypes: float64(3), object(38)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "exchange_rates.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>[Australian dollar ]</th>\n",
       "      <th>[Bulgarian lev ]</th>\n",
       "      <th>[Brazilian real ]</th>\n",
       "      <th>[Canadian dollar ]</th>\n",
       "      <th>[Swiss franc ]</th>\n",
       "      <th>[Chinese yuan renminbi ]</th>\n",
       "      <th>[Cypriot pound ]</th>\n",
       "      <th>[Czech koruna ]</th>\n",
       "      <th>[Danish krone ]</th>\n",
       "      <th>...</th>\n",
       "      <th>[Romanian leu ]</th>\n",
       "      <th>[Russian rouble ]</th>\n",
       "      <th>[Swedish krona ]</th>\n",
       "      <th>[Singapore dollar ]</th>\n",
       "      <th>[Slovenian tolar ]</th>\n",
       "      <th>[Slovak koruna ]</th>\n",
       "      <th>[Thai baht ]</th>\n",
       "      <th>[Turkish lira ]</th>\n",
       "      <th>US_dollar</th>\n",
       "      <th>[South African rand ]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1999-01-04</td>\n",
       "      <td>1.9100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.8004</td>\n",
       "      <td>1.6168</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.58231</td>\n",
       "      <td>35.107</td>\n",
       "      <td>7.4501</td>\n",
       "      <td>...</td>\n",
       "      <td>1.3111</td>\n",
       "      <td>25.2875</td>\n",
       "      <td>9.4696</td>\n",
       "      <td>1.9554</td>\n",
       "      <td>189.0450</td>\n",
       "      <td>42.991</td>\n",
       "      <td>42.6799</td>\n",
       "      <td>0.3723</td>\n",
       "      <td>1.1789</td>\n",
       "      <td>6.9358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1999-01-05</td>\n",
       "      <td>1.8944</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7965</td>\n",
       "      <td>1.6123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.58230</td>\n",
       "      <td>34.917</td>\n",
       "      <td>7.4495</td>\n",
       "      <td>...</td>\n",
       "      <td>1.3168</td>\n",
       "      <td>26.5876</td>\n",
       "      <td>9.4025</td>\n",
       "      <td>1.9655</td>\n",
       "      <td>188.7750</td>\n",
       "      <td>42.848</td>\n",
       "      <td>42.5048</td>\n",
       "      <td>0.3728</td>\n",
       "      <td>1.1790</td>\n",
       "      <td>6.7975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1999-01-06</td>\n",
       "      <td>1.8820</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7711</td>\n",
       "      <td>1.6116</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.58200</td>\n",
       "      <td>34.850</td>\n",
       "      <td>7.4452</td>\n",
       "      <td>...</td>\n",
       "      <td>1.3168</td>\n",
       "      <td>27.4315</td>\n",
       "      <td>9.3050</td>\n",
       "      <td>1.9699</td>\n",
       "      <td>188.7000</td>\n",
       "      <td>42.778</td>\n",
       "      <td>42.6949</td>\n",
       "      <td>0.3722</td>\n",
       "      <td>1.1743</td>\n",
       "      <td>6.7307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1999-01-07</td>\n",
       "      <td>1.8474</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7602</td>\n",
       "      <td>1.6165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.58187</td>\n",
       "      <td>34.886</td>\n",
       "      <td>7.4431</td>\n",
       "      <td>...</td>\n",
       "      <td>1.3092</td>\n",
       "      <td>26.9876</td>\n",
       "      <td>9.1800</td>\n",
       "      <td>1.9436</td>\n",
       "      <td>188.8000</td>\n",
       "      <td>42.765</td>\n",
       "      <td>42.1678</td>\n",
       "      <td>0.3701</td>\n",
       "      <td>1.1632</td>\n",
       "      <td>6.8283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1999-01-08</td>\n",
       "      <td>1.8406</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7643</td>\n",
       "      <td>1.6138</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.58187</td>\n",
       "      <td>34.938</td>\n",
       "      <td>7.4433</td>\n",
       "      <td>...</td>\n",
       "      <td>1.3143</td>\n",
       "      <td>27.2075</td>\n",
       "      <td>9.1650</td>\n",
       "      <td>1.9537</td>\n",
       "      <td>188.8400</td>\n",
       "      <td>42.560</td>\n",
       "      <td>42.5590</td>\n",
       "      <td>0.3718</td>\n",
       "      <td>1.1659</td>\n",
       "      <td>6.7855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Time [Australian dollar ] [Bulgarian lev ] [Brazilian real ]  \\\n",
       "0 1999-01-04               1.9100              NaN               NaN   \n",
       "1 1999-01-05               1.8944              NaN               NaN   \n",
       "2 1999-01-06               1.8820              NaN               NaN   \n",
       "3 1999-01-07               1.8474              NaN               NaN   \n",
       "4 1999-01-08               1.8406              NaN               NaN   \n",
       "\n",
       "  [Canadian dollar ] [Swiss franc ] [Chinese yuan renminbi ] [Cypriot pound ]  \\\n",
       "0             1.8004         1.6168                      NaN          0.58231   \n",
       "1             1.7965         1.6123                      NaN          0.58230   \n",
       "2             1.7711         1.6116                      NaN          0.58200   \n",
       "3             1.7602         1.6165                      NaN          0.58187   \n",
       "4             1.7643         1.6138                      NaN          0.58187   \n",
       "\n",
       "  [Czech koruna ] [Danish krone ]  ... [Romanian leu ] [Russian rouble ]  \\\n",
       "0          35.107          7.4501  ...          1.3111           25.2875   \n",
       "1          34.917          7.4495  ...          1.3168           26.5876   \n",
       "2          34.850          7.4452  ...          1.3168           27.4315   \n",
       "3          34.886          7.4431  ...          1.3092           26.9876   \n",
       "4          34.938          7.4433  ...          1.3143           27.2075   \n",
       "\n",
       "  [Swedish krona ] [Singapore dollar ] [Slovenian tolar ] [Slovak koruna ]  \\\n",
       "0           9.4696              1.9554           189.0450           42.991   \n",
       "1           9.4025              1.9655           188.7750           42.848   \n",
       "2           9.3050              1.9699           188.7000           42.778   \n",
       "3           9.1800              1.9436           188.8000           42.765   \n",
       "4           9.1650              1.9537           188.8400           42.560   \n",
       "\n",
       "  [Thai baht ] [Turkish lira ] US_dollar  [South African rand ]  \n",
       "0      42.6799          0.3723    1.1789                 6.9358  \n",
       "1      42.5048          0.3728    1.1790                 6.7975  \n",
       "2      42.6949          0.3722    1.1743                 6.7307  \n",
       "3      42.1678          0.3701    1.1632                 6.8283  \n",
       "4      42.5590          0.3718    1.1659                 6.7855  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exchange_rates.rename(columns={'[US dollar ]': 'US_dollar',\n",
    "                               'Period\\\\Unit:': 'Time'},\n",
    "                      inplace=True)\n",
    "exchange_rates['Time'] = pd.to_datetime(exchange_rates['Time'])\n",
    "exchange_rates.sort_values('Time', inplace=True)\n",
    "exchange_rates.reset_index(drop=True, inplace=True)\n",
    "exchange_rates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-         62\n",
       "1.2276     9\n",
       "1.1215     8\n",
       "1.1305     7\n",
       "1.0867     6\n",
       "          ..\n",
       "1.4032     1\n",
       "1.3330     1\n",
       "1.3358     1\n",
       "1.3521     1\n",
       "1.1887     1\n",
       "Name: US_dollar, Length: 3528, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euro_to_dollar = exchange_rates[['Time', 'US_dollar']].copy()\n",
    "euro_to_dollar['US_dollar'].value_counts() # 62 '-' characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5637 entries, 0 to 5698\n",
      "Data columns (total 2 columns):\n",
      " #   Column     Non-Null Count  Dtype         \n",
      "---  ------     --------------  -----         \n",
      " 0   Time       5637 non-null   datetime64[ns]\n",
      " 1   US_dollar  5637 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(1)\n",
      "memory usage: 132.1 KB\n"
     ]
    }
   ],
   "source": [
    "euro_to_dollar = euro_to_dollar[euro_to_dollar['US_dollar'] != '-']\n",
    "euro_to_dollar['US_dollar'] = euro_to_dollar['US_dollar'].astype(float)\n",
    "euro_to_dollar.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rolling Mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.plot(euro_to_dollar['Time'], euro_to_dollar['US_dollar'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "\n",
    "plt.subplot(3,2,1)\n",
    "plt.plot(euro_to_dollar['Time'], euro_to_dollar['US_dollar'])\n",
    "plt.title('Original values', weight='bold')\n",
    "\n",
    "for i, rolling_mean in zip([2, 3, 4, 5, 6],\n",
    "                           [7, 30, 50, 100, 365]):\n",
    "    plt.subplot(3,2,i)\n",
    "    plt.plot(euro_to_dollar['Time'],\n",
    "             euro_to_dollar['US_dollar'].rolling(rolling_mean).mean())\n",
    "    plt.title('Rolling Window:' + str(rolling_mean), weight='bold')\n",
    "    \n",
    "plt.tight_layout() # Auto-adjusts the padding between subplots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>US_dollar</th>\n",
       "      <th>rolling_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1999-01-04</td>\n",
       "      <td>1.1789</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1999-01-05</td>\n",
       "      <td>1.1790</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1999-01-06</td>\n",
       "      <td>1.1743</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1999-01-07</td>\n",
       "      <td>1.1632</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1999-01-08</td>\n",
       "      <td>1.1659</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5694</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>1.2296</td>\n",
       "      <td>1.211170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5695</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>1.2271</td>\n",
       "      <td>1.212530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5696</th>\n",
       "      <td>2021-01-06</td>\n",
       "      <td>1.2338</td>\n",
       "      <td>1.213987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5697</th>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>1.2276</td>\n",
       "      <td>1.215357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5698</th>\n",
       "      <td>2021-01-08</td>\n",
       "      <td>1.2250</td>\n",
       "      <td>1.216557</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5637 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Time  US_dollar  rolling_mean\n",
       "0    1999-01-04     1.1789           NaN\n",
       "1    1999-01-05     1.1790           NaN\n",
       "2    1999-01-06     1.1743           NaN\n",
       "3    1999-01-07     1.1632           NaN\n",
       "4    1999-01-08     1.1659           NaN\n",
       "...         ...        ...           ...\n",
       "5694 2021-01-04     1.2296      1.211170\n",
       "5695 2021-01-05     1.2271      1.212530\n",
       "5696 2021-01-06     1.2338      1.213987\n",
       "5697 2021-01-07     1.2276      1.215357\n",
       "5698 2021-01-08     1.2250      1.216557\n",
       "\n",
       "[5637 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euro_to_dollar['rolling_mean'] = euro_to_dollar['US_dollar'].rolling(30).mean()\n",
    "euro_to_dollar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Coming Up With an Idea\n",
    "\n",
    "Here are a few story ideas for our data:\n",
    "\n",
    "- We show how the euro-dollar rate has changed during the coronavirus pandemic. We can show the 2020 data and the 2016-2019 data as a baseline. We can use a line plot.\n",
    "- We show how the euro-dollar rate changed during the 2007-2008's financial crisis. We can also show the data for 2016 and 2009 for comparison. We can use a line plot.\n",
    "- We show comparatively how the euro-dollar rate changed under the last three US presidents (George W. Bush (2001-2009), Barack Obama (2009-2017), and Donald Trump (2017-2021)). We can use a line plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Storytelling Data Visualization Examples\n",
    "\n",
    "### Financial Crisis Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "financial_crisis = euro_to_dollar.copy(\n",
    "                   )[(euro_to_dollar['Time'].dt.year >= 2006\n",
    "                   ) & (euro_to_dollar['Time'].dt.year <= 2009)]\n",
    "financial_crisis_7_8 = euro_to_dollar.copy(\n",
    "                   )[(euro_to_dollar.Time.dt.year >= 2007\n",
    "                   ) & (euro_to_dollar.Time.dt.year <= 2008)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Adding the FiveThirtyEight style\n",
    "import matplotlib.style as style\n",
    "style.use('fivethirtyeight')\n",
    "\n",
    "### Adding the plot\n",
    "fig,ax = plt.subplots(figsize=(8,3))\n",
    "ax.plot(financial_crisis['Time'],\n",
    "        financial_crisis['rolling_mean'],\n",
    "        linewidth=1, color='#A6D785')\n",
    "\n",
    "### Highlighting the 2007-2008 period\n",
    "ax.plot(financial_crisis_7_8['Time'],\n",
    "        financial_crisis_7_8['rolling_mean'],\n",
    "        linewidth=3, color='#e23d28')\n",
    "\n",
    "### Highlihting the peak of the crisis\n",
    "ax.axvspan(xmin=13950, xmax=14140, ymin=0.09,\n",
    "           alpha=0.3, color='grey')\n",
    "\n",
    "### Adding separate tick labels\n",
    "ax.set_xticklabels([])\n",
    "ax.set_yticklabels([])\n",
    "\n",
    "x = 13110\n",
    "for year in ['2006', '2007', '2008', '2009', '2010']:\n",
    "    ax.text(x, 1.13, year, alpha=0.5, fontsize=11)\n",
    "    x += 365\n",
    "\n",
    "y = 1.193   \n",
    "for rate in ['1.2', '1.3', '1.4', '1.5']:\n",
    "    ax.text(13020, y, rate, alpha=0.5, fontsize=11)\n",
    "    y += 0.1\n",
    "\n",
    "### Adding a title and a subtitle    \n",
    "ax.text(13020, 1.67, \"Euro-USD rate peaked at 1.59 during 2007-2008's financial crisis\",\n",
    "        weight='bold')\n",
    "ax.text(13020, 1.63, 'Euro-USD exchange rates between 2006 and 2010',\n",
    "        size=12)\n",
    "\n",
    "### Adding a signature\n",
    "ax.text(13020, 1.07, '©DATAQUEST' + ' '*94 + 'Source: European Central Bank',\n",
    "        color = '#f0f0f0', backgroundcolor = '#4d4d4d',\n",
    "        size=10)\n",
    "\n",
    "### Add some transparency to the grid\n",
    "ax.grid(alpha=0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Three US Presidencies Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "bush_obama_trump = euro_to_dollar.copy(\n",
    "                   )[(euro_to_dollar['Time'].dt.year >= 2001) & (euro_to_dollar['Time'].dt.year < 2021)]\n",
    "bush = bush_obama_trump.copy(\n",
    "       )[bush_obama_trump['Time'].dt.year < 2009]\n",
    "obama = bush_obama_trump.copy(\n",
    "       )[(bush_obama_trump['Time'].dt.year >= 2009) & (bush_obama_trump['Time'].dt.year < 2017)]\n",
    "trump = bush_obama_trump.copy(\n",
    "       )[(bush_obama_trump['Time'].dt.year >= 2017) & (bush_obama_trump['Time'].dt.year < 2021)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, you'll notice we used matplotlib's functional approach to build the graphs. We use this approach because it offers more\n",
    "flexibility in arranging the subplots:\n",
    "\n",
    "- We first build three of the graphs on a 2-by-3 grid (this grid should have six subplots, but we only build three; the bottom row remains empty).\n",
    "- We then build only the _bottom_ graph of a 2-by-1 grid (this grid should have two subplots; the top row remains empty).\n",
    "- The two grids are merged, and we end up with three graphs on the top row and one graph on the bottom row. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alex/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:26: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "/home/alex/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:38: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "/home/alex/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:50: UserWarning: FixedFormatter should only be used together with FixedLocator\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Adding the FiveThirtyEight style\n",
    "style.use('fivethirtyeight')\n",
    "\n",
    "### Adding the subplots\n",
    "plt.figure(figsize=(12, 6))\n",
    "ax1 = plt.subplot(2,3,1)\n",
    "ax2 = plt.subplot(2,3,2)\n",
    "ax3 = plt.subplot(2,3,3)\n",
    "ax4 = plt.subplot(2,1,2)\n",
    "axes = [ax1, ax2, ax3, ax4]\n",
    "\n",
    "### Changes to all the subplots\n",
    "for ax in axes:\n",
    "    ax.set_ylim(0.8, 1.7)\n",
    "    ax.set_yticks([1.0, 1.2, 1.4, 1.6])\n",
    "    ax.set_yticklabels(['1.0', '1.2','1.4', '1.6'],\n",
    "                   alpha=0.3)\n",
    "    ax.grid(alpha=0.5)    \n",
    "    \n",
    "\n",
    "### Ax1: Bush\n",
    "ax1.plot(bush['Time'], bush['rolling_mean'],\n",
    "        color='#BF5FFF')\n",
    "ax1.set_xticklabels(['', '2001', '', '2003', '', '2005', '',\n",
    "                     '2007', '', '2009'],\n",
    "                   alpha=0.3)\n",
    "ax1.text(12300, 1.92, 'BUSH', fontsize=18, weight='bold',\n",
    "        color='#BF5FFF')\n",
    "ax1.text(12000, 1.8, '(2001-2009)', weight='bold',\n",
    "        alpha=0.3)\n",
    "\n",
    "\n",
    "### Ax2: Obama\n",
    "ax2.plot(obama['Time'], obama['rolling_mean'],\n",
    "        color='#ffa500')\n",
    "ax2.set_xticklabels(['', '2009', '', '2011', '', '2013', '',\n",
    "                     '2015', '', '2017'],\n",
    "                   alpha=0.3)\n",
    "ax2.text(15100, 1.92, 'OBAMA', fontsize=18, weight='bold',\n",
    "        color='#ffa500')\n",
    "ax2.text(14950, 1.8, '(2009-2017)', weight='bold',\n",
    "         alpha=0.3)\n",
    "\n",
    "\n",
    "### Ax3: Trump\n",
    "ax3.plot(trump['Time'], trump['rolling_mean'],\n",
    "        color='#00B2EE')\n",
    "ax3.set_xticklabels(['2017', '', '2018', '', '2019', '',\n",
    "                     '2020', '', '2021'],\n",
    "                   alpha=0.3)\n",
    "ax3.text(17650, 1.92, 'TRUMP', fontsize=18, weight='bold',\n",
    "        color='#00B2EE')\n",
    "ax3.text(17560, 1.8, '(2017-2021)', weight='bold',\n",
    "         alpha=0.3)\n",
    "\n",
    "### Ax4: Bush-Obama-Trump\n",
    "ax4.plot(bush['Time'], bush['rolling_mean'],\n",
    "        color='#BF5FFF')\n",
    "ax4.plot(obama['Time'], obama['rolling_mean'],\n",
    "        color='#ffa500')\n",
    "ax4.plot(trump['Time'], trump['rolling_mean'],\n",
    "        color='#00B2EE')\n",
    "ax4.grid(alpha=0.5)\n",
    "ax4.set_xticks([])\n",
    "\n",
    "### Adding a title and a subtitle\n",
    "ax1.text(10800, 2.35, 'EURO-USD rate averaged 1.22 under the last three US presidents',\n",
    "         fontsize=20, weight='bold')\n",
    "ax1.text(10800, 2.14, '''EURO-USD exchange rates under George W. Bush (2001 - 2009), Barack Obama (2009-2017),\n",
    "and Donald Trump (2017-2021)''',\n",
    "        fontsize=16)\n",
    "\n",
    "### Adding a signature\n",
    "ax4.text(10700, 0.65, '©DATAQUEST' + ' '*103 + 'Source: European Central Bank',\n",
    "        color = '#f0f0f0', backgroundcolor = '#4d4d4d',\n",
    "        size=14)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
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 },
 "nbformat": 4,
 "nbformat_minor": 4
}